Online egomotion estimation of RGB-D sensors using spherical harmonics

Osteen, Philip R.; Owens, Jason; Kessens, Chad C.

doi:10.1109/icra.2012.6225098

Cited by 6 publications

(5 citation statements)

References 11 publications

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“…Therefore, low RMSE drift values indicate a continously high tracking quality. For evaluation, we chose the four "desk" datasets, because they contain real world scenery with structure and texture, different velocity profiles, and have recently been used by several other authors for evaluation [7], [10], [22], [33], [34].…”

Section: B Tum Rgb-d Benchmark Sequencesmentioning

confidence: 99%

Robust odometry estimation for RGB-D cameras

Kerl

Sturm

Cremers

2013

2013 IEEE International Conference on Robotics and Automation

499

411

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Abstract-The goal of our work is to provide a fast and accurate method to estimate the camera motion from RGB-D images. Our approach registers two consecutive RGB-D frames directly upon each other by minimizing the photometric error. We estimate the camera motion using non-linear minimization in combination with a coarse-to-fine scheme. To allow for noise and outliers in the image data, we propose to use a robust error function that reduces the influence of large residuals. Furthermore, our formulation allows for the inclusion of a motion model which can be based on prior knowledge, temporal filtering, or additional sensors like an IMU. Our method is attractive for robots with limited computational resources as it runs in real-time on a single CPU core and has a small, constant memory footprint. In an extensive set of experiments carried out both on a benchmark dataset and synthetic data, we demonstrate that our approach is more accurate and robust than previous methods. We provide our software under an open source license.

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Section: B Tum Rgb-d Benchmark Sequencesmentioning

confidence: 99%

Robust odometry estimation for RGB-D cameras

Kerl

Sturm

Cremers

2013

2013 IEEE International Conference on Robotics and Automation

499

411

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“…These solutions are either dense [15], [16], [17], [18] or feature based [19], [20], [21], [22], [23] approaches.…”

Section: Related Workmentioning

confidence: 98%

“…Whelan et al [17] extends the solution of Steinbruecker et al with a GPU implementation of the algorithm and the incorporation of feature based visual front-ends to the pose estimation process. Osteen et al [18] take an innovative direction proposing a Visual Odometry algorithm that works by computing correlations between the frequency domain representations of the scene normals.…”

Section: Related Workmentioning

confidence: 99%

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A Fast Feature Tracking Algorithm for Visual Odometry and Mapping Based on RGB-D Sensors

Silva

Gonçalves

2014

2014 27th SIBGRAPI Conference on Graphics, Patterns and Images

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The recent introduction of low cost sensors such as the Kinect allows the design of real-time applications (i.e. for Robotics) that exploit novel capabilities. One such application is Visual Odometry, a fundamental module of any robotic platform that uses the synchronized color/depth streams captured by these devices to build a map representation of the environment at the same that the robot is localized within the map. Aiming to minimize error accumulation inherent to the process of robot localization, we design a visual feature tracker that works as the front-end of a Visual Odometry system for RGB-D sensors. Feature points are added to the tracker selectively based on pre-specified criteria such as the number of currently active points and their spatial distribution throughout the image. Our proposal is a tracking strategy that allows real-time camera pose computation (average of 24.847 ms per frame) despite the fact that no specialized hardware (such as modern GPUs) is employed. Experiments carried out on publicly available benchmark and datasets demonstrate the usefulness of the method, which achieved RMSE rates superior to the state-of-the-art RGB-D SLAM algorithm.

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“…For frames close to each other, they achieve enhanced runtime performance and accuracy compared to Generalized ICP [21]. Using the distribution of normals, Osteen et al [22] improve the initialization of ICP by efficiently computing the difference in orientation between two frames, which allows a substantial drift reduction. These approaches work well for computing the transformation for small incremental changes between consecutive frames, but they are of limited applicability for global localization in a map.…”

Section: Related Workmentioning

confidence: 99%

Fast and Robust Feature Matching for RGB-D Based Localization

Heredia,

Endres,

Burgard

et al. 2015

Preprint

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In this paper we present a novel approach to global localization using an RGB-D camera in maps of visual features. For large maps, the performance of pure image matching techniques decays in terms of robustness and computational cost. Particularly, repeated occurrences of similar features due to repeating structure in the world (e.g., doorways, chairs, etc.) or missing associations between observations pose critical challenges to visual localization. We address these challenges using a two-step approach. We first estimate a candidate pose using few correspondences between features of the current camera frame and the feature map. The initial set of correspondences is established by proximity in feature space. The initial pose estimate is used in the second step to guide spatial matching of features in 3D, i.e., searching for associations where the image features are expected to be found in the map. A RANSAC algorithm is used to compute a fine estimation of the pose from the correspondences. Our approach clearly outperforms localization based on feature matching exclusively in feature space, both in terms of estimation accuracy and robustness to failure and allows for global localization in real time (30 Hz).

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Online egomotion estimation of RGB-D sensors using spherical harmonics

Cited by 6 publications

References 11 publications

Robust odometry estimation for RGB-D cameras

Robust odometry estimation for RGB-D cameras

A Fast Feature Tracking Algorithm for Visual Odometry and Mapping Based on RGB-D Sensors

Fast and Robust Feature Matching for RGB-D Based Localization

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